-------------------------------------------------------------------------------------------------------------
      name:  <unnamed>
       log:  C:\Users\a15014rs\Dropbox\Sexism and Candidates\Analysis\BJPS replication files\Australia_log_fi
> le.log
  log type:  text
 opened on:  20 Mar 2025, 11:24:59

. 
. ** Recoding and merging data files
. 
. cd "C:\Users\a15014rs\Dropbox\Sexism and Candidates\Data\Australia"
C:\Users\a15014rs\Dropbox\Sexism and Candidates\Data\Australia

. 
. import excel "Australia_candidates.xlsx", firstrow clear

. 
. ** drop variables not required
. drop StateAb CandidateID Surname GivenNm Link

. 
. ** keep main parties
. keep if PartyNm=="Liberal"|PartyNm=="Country Liberals (NT"|PartyNm=="Liberal National Party of Queensland"|
> PartyNm=="National Party"| ///
>         PartyNm=="The Nationals"|PartyNm=="Australian Labor Party"|PartyNm=="Australian Labor Party (Northe
> rn Territory) Branch"|PartyNm=="Labor"| ///
>         PartyNm=="The Greens"|PartyNm=="The Greens (VIC)"|PartyNm=="The Greens (WA)"
(594 observations deleted)

. 
. ** candidate gender
. gen candidate_gender=1 if Gender=="F"
(292 missing values generated)

. replace candidate_gender=0 if Gender=="M"
(292 real changes made)

. label define genderlab 0 "men" 1 "women"

. label values candidate_gender genderlab

. drop Gender

. 
. ** distance from the winner in 2016
. destring ElectionVoteShare, dpcomma ignore("%" "#N/D" ",,,") generate(voteshare2016)
ElectionVoteShare: characters % # N / D , removed; voteshare2016 generated as int
(64 missing values generated)

. gen voteshare2016_v2=voteshare2016/100
(64 missing values generated)

. drop ElectionVoteShare voteshare2016

. rename voteshare2016_v2 voteshare2016

. 
. egen max_share = max(voteshare2016), by(DivisionID)
(24 missing values generated)

. gen winner2016 = 1 if max_share==voteshare2016
(295 missing values generated)

. replace winner2016 = 0 if winner2016!=1
(295 real changes made)

. 
. gen distance=max_share-voteshare2016
(64 missing values generated)

. /* give median distance if party did not stand in 2016 */
. /* give median distance for new constituencies */
. tabstat distance, stats(median)

    variable |       p50
-------------+----------
    distance |     15.85
------------------------

. replace distance=15.85 if distance==.
(64 real changes made)

. 
. *set previous vote share to zero if they did not stand in 2016*
. replace voteshare2016=0 if voteshare2016==.
(64 real changes made)

. 
. drop max_share

. 
. ** winner 2019
. destring VoteShare, ignore("%" ",") generate(voteshare2019)
VoteShare: characters % , removed; voteshare2019 generated as int

. gen voteshare2019_v2=voteshare2019/100

. drop voteshare2019

. rename voteshare2019_v2 voteshare2019

. egen max_share = max(voteshare2019), by(DivisionID)

. gen winner2019 = 1 if max_share==voteshare2019
(311 missing values generated)

. replace winner2019 = 1 if max_share==voteshare2019
(0 real changes made)

. replace winner2019 = 0 if winner2019==.
(311 real changes made)

. 
. ** descriptives candidates (Table 4)
. tab candidate_gender

candidate_g |
      ender |      Freq.     Percent        Cum.
------------+-----------------------------------
        men |        292       63.20       63.20
      women |        170       36.80      100.00
------------+-----------------------------------
      Total |        462      100.00

. replace Incumbent="Y" if Incumbent=="y"
(1 real change made)

. tab Incumbent candidate_gender, row

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

           |   candidate_gender
Incumbent  |       men      women |     Total
-----------+----------------------+----------
         N |       199        138 |       337 
           |     59.05      40.95 |    100.00 
-----------+----------------------+----------
         Y |        93         32 |       125 
           |     74.40      25.60 |    100.00 
-----------+----------------------+----------
     Total |       292        170 |       462 
           |     63.20      36.80 |    100.00 


. tab winner2019 candidate_gender, row

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

           |   candidate_gender
winner2019 |       men      women |     Total
-----------+----------------------+----------
         0 |       183        128 |       311 
           |     58.84      41.16 |    100.00 
-----------+----------------------+----------
         1 |       109         42 |       151 
           |     72.19      27.81 |    100.00 
-----------+----------------------+----------
     Total |       292        170 |       462 
           |     63.20      36.80 |    100.00 


. ttest distance, by(candidate_gender)

Two-sample t test with equal variances
------------------------------------------------------------------------------
   Group |     Obs        Mean    Std. Err.   Std. Dev.   [95% Conf. Interval]
---------+--------------------------------------------------------------------
     men |     292     17.5136    1.013119    17.31219    15.51963    19.50757
   women |     170    18.87718    1.199604    15.64092    16.50904    21.24532
---------+--------------------------------------------------------------------
combined |     462    18.01535     .777533    16.71244     16.4874    19.54329
---------+--------------------------------------------------------------------
    diff |           -1.363581    1.612796               -4.532941    1.805779
------------------------------------------------------------------------------
    diff = mean(men) - mean(women)                                t =  -0.8455
Ho: diff = 0                                     degrees of freedom =      460

    Ha: diff < 0                 Ha: diff != 0                 Ha: diff > 0
 Pr(T < t) = 0.1991         Pr(|T| > |t|) = 0.3983          Pr(T > t) = 0.8009

. tab candidate_gender if PartyNm=="Liberal"|PartyNm=="Country Liberals (NT"| ///
>         PartyNm=="Liberal National Party of Queensland"|PartyNm=="National Party"|PartyNm=="The Nationals"

candidate_g |
      ender |      Freq.     Percent        Cum.
------------+-----------------------------------
        men |        118       73.75       73.75
      women |         42       26.25      100.00
------------+-----------------------------------
      Total |        160      100.00

. tab candidate_gender if PartyNm=="Australian Labor Party"| ///
>         PartyNm=="Australian Labor Party (Northern Territory) Branch"|PartyNm=="Labor"

candidate_g |
      ender |      Freq.     Percent        Cum.
------------+-----------------------------------
        men |         85       56.29       56.29
      women |         66       43.71      100.00
------------+-----------------------------------
      Total |        151      100.00

. tab candidate_gender if PartyNm=="The Greens"|PartyNm=="The Greens (VIC)"|PartyNm=="The Greens (WA)"

candidate_g |
      ender |      Freq.     Percent        Cum.
------------+-----------------------------------
        men |         89       58.94       58.94
      women |         62       41.06      100.00
------------+-----------------------------------
      Total |        151      100.00

. 
. ** Women races
. gen candidate_gender2=1 if candidate_gender==1 /*women*/
(292 missing values generated)

. replace candidate_gender2=2 if candidate_gender==0 /*men*/
(292 real changes made)

. fre candidate_gender

candidate_gender
-------------------------------------------------------------
                |      Freq.    Percent      Valid       Cum.
----------------+--------------------------------------------
Valid   0 men   |        292      63.20      63.20      63.20
        1 women |        170      36.80      36.80     100.00
        Total   |        462     100.00     100.00           
-------------------------------------------------------------

. egen women_race = min(candidate_gender2), by(DivisionID) /*minimum value within district*/

. egen women_only = max(candidate_gender2), by(DivisionID) /*maximum value within district*/

. codebook DivisionID

-------------------------------------------------------------------------------------------------------------
DivisionID                                                                                         DivisionID
-------------------------------------------------------------------------------------------------------------

                  type:  numeric (int)

                 range:  [101,325]                    units:  1
         unique values:  151                      missing .:  0/462

                  mean:   194.649
              std. dev:   62.0125

           percentiles:        10%       25%       50%       75%       90%
                               118       145     187.5       233       307

. codebook DivisionID if women_race==2

-------------------------------------------------------------------------------------------------------------
DivisionID                                                                                         DivisionID
-------------------------------------------------------------------------------------------------------------

                  type:  numeric (int)

                 range:  [103,318]                    units:  1
         unique values:  35                       missing .:  0/103

                  mean:   183.573
              std. dev:   65.3656

           percentiles:        10%       25%       50%       75%       90%
                               111       131       174       234       307

. codebook DivisionID if women_only==1

-------------------------------------------------------------------------------------------------------------
DivisionID                                                                                         DivisionID
-------------------------------------------------------------------------------------------------------------

                  type:  numeric (int)

                 range:  [146,203]                    units:  1
         unique values:  6                        missing .:  0/18

            tabulation:  Freq.  Value
                             3  146
                             3  172
                             3  182
                             3  186
                             3  188
                             3  203

. 
. 
. saveold "Australia_candidates_data.dta", replace
(saving in Stata 13 format)
(FYI, saveold has options version(12) and version(11) that write files in older Stata formats)
file Australia_candidates_data.dta saved

. 
. ** Vote choice data
. use "aes19_unrestricted.dta", clear

. 
. ** recode division name for merging
. rename CED_AEC DivisionID

. 
. ** merge with candidates data
. sort DivisionID

. 
. ** merge with candidates data
. joinby DivisionID using "Australia_candidates_data.dta"

. 
. ** respondent vote choice
. gen vote_choice=1 if B9_1==1&PartyNm=="Liberal"|PartyNm=="Country Liberals (NT)"| ///
>         PartyNm=="Liberal National Party of Queensland"
(5,573 missing values generated)

. replace vote_choice=1 if B9_1==2&PartyNm=="Australian Labor Party"| ///
>         PartyNm=="Australian Labor Party (Northern Territory) Branch"|PartyNm=="Labor"
(1,165 real changes made)

. replace vote_choice=1 if B9_1==3&PartyNm=="National Party"|PartyNm=="The Nationals"
(317 real changes made)

. replace vote_choice=1 if B9_1==4&PartyNm=="The Greens"|PartyNm=="The Greens (VIC)"|PartyNm=="The Greens (WA
> )"
(890 real changes made)

. replace vote_choice=0 if vote_choice==.
(3,201 real changes made)

. replace vote_choice=. if B9_1==6|B9_1==999
(319 real changes made, 319 to missing)

. 
. saveold "Australia_data.dta", replace
(saving in Stata 13 format)
(FYI, saveold has options version(12) and version(11) that write files in older Stata formats)
file Australia_data.dta saved

. 
. ***********************************************************************************************************
> *************
. 
. ** Analysis
. 
. cd "C:\Users\a15014rs\Dropbox\Sexism and Candidates\"
C:\Users\a15014rs\Dropbox\Sexism and Candidates

. 
. use "Data\Australia\Australia_data.dta", clear

. 
. * Create variable for women races
. fre candidate_gender

candidate_gender
-------------------------------------------------------------
                |      Freq.    Percent      Valid       Cum.
----------------+--------------------------------------------
Valid   0 men   |       4164      62.64      62.64      62.64
        1 women |       2483      37.36      37.36     100.00
        Total   |       6647     100.00     100.00           
-------------------------------------------------------------

. *egen women_race = min(candidate_gender2), by(DivisionID)
. *egen women_only = max(candidate_gender2), by(DivisionID)
. gen sample_races=1 if women_race==1&women_only==2
(1,780 missing values generated)

. replace sample_races=0 if sample_races==.
(1,780 real changes made)

. 
. codebook DivisionID if sample_races==1

-------------------------------------------------------------------------------------------------------------
DivisionID                                              Commonwealth Electoral Division 2018 - AEC code frame
-------------------------------------------------------------------------------------------------------------

                  type:  numeric (double)
                 label:  CED_AEC

                 range:  [101,325]                    units:  1
         unique values:  110                      missing .:  0/4,867

              examples:  135   New England
                         171   McPherson
                         214   Goldstein
                         239   Forrest

. **all-women races
. codebook DivisionID if women_race==2

-------------------------------------------------------------------------------------------------------------
DivisionID                                              Commonwealth Electoral Division 2018 - AEC code frame
-------------------------------------------------------------------------------------------------------------

                  type:  numeric (double)
                 label:  CED_AEC

                 range:  [103,318]                    units:  1
         unique values:  35                       missing .:  0/1,444

              examples:  121   Grayndler
                         139   Parkes
                         177   Ryan
                         234   Wills

. **all-male races
. codebook DivisionID if women_only==1

-------------------------------------------------------------------------------------------------------------
DivisionID                                              Commonwealth Electoral Division 2018 - AEC code frame
-------------------------------------------------------------------------------------------------------------

                  type:  numeric (double)
                 label:  CED_AEC

                 range:  [146,203]                    units:  1
         unique values:  6                        missing .:  0/336

            tabulation:  Freq.   Numeric  Label
                            54       146  Robertson
                            60       172  Moncrieff
                            66       182  Boothby
                            84       186  Kingston
                            66       188  Mayo
                             6       203  Calwell

. 
. ** HOstile sexism - sample size
. tab E9_1, missing

  E9_1. Please say whether |
you strongly agree, agree, |
      disagree or strongly |
                   disagre |      Freq.     Percent        Cum.
---------------------------+-----------------------------------
            Strongly agree |        637        9.58        9.58
                     Agree |      2,056       30.93       40.51
Neither agree nor disagree |      1,773       26.67       67.19
                  Disagree |      1,257       18.91       86.10
         Strongly disagree |        810       12.19       98.28
              Item Skipped |        114        1.72      100.00
---------------------------+-----------------------------------
                     Total |      6,647      100.00

. tab E9_2, missing

  E9_2. Please say whether |
you strongly agree, agree, |
      disagree or strongly |
                   disagre |      Freq.     Percent        Cum.
---------------------------+-----------------------------------
            Strongly agree |        236        3.55        3.55
                     Agree |        767       11.54       15.09
Neither agree nor disagree |      2,044       30.75       45.84
                  Disagree |      1,983       29.83       75.67
         Strongly disagree |      1,491       22.43       98.10
              Item Skipped |        126        1.90      100.00
---------------------------+-----------------------------------
                     Total |      6,647      100.00

. tab E9_3, missing

  E9_3. Please say whether |
you strongly agree, agree, |
      disagree or strongly |
                   disagre |      Freq.     Percent        Cum.
---------------------------+-----------------------------------
            Strongly agree |        277        4.17        4.17
                     Agree |        723       10.88       15.04
Neither agree nor disagree |      1,846       27.77       42.82
                  Disagree |      1,968       29.61       72.42
         Strongly disagree |      1,716       25.82       98.24
              Item Skipped |        117        1.76      100.00
---------------------------+-----------------------------------
                     Total |      6,647      100.00

. 
. ** Sexism measures (hostile only)
. recode E9_1 (1=5) (2=4) (3=3) (4=2) (5=1) (999=.), generate(innocent)
(4874 differences between E9_1 and innocent)

. recode E9_2 (1=5) (2=4) (3=3) (4=2) (5=1) (999=.), generate(appreciate)
(4603 differences between E9_2 and appreciate)

. recode E9_3 (1=5) (2=4) (3=3) (4=2) (5=1) (999=.), generate(control)
(4801 differences between E9_3 and control)

. 
. alpha innocent appreciate control /*0.829 */

Test scale = mean(unstandardized items)

Average interitem covariance:     .7786827
Number of items in the scale:            3
Scale reliability coefficient:      0.8294

. 
. * scale to range - 2 to +2 (positive values = high in sexism)
. recode appreciate 1=-2 2=-1 3=0 4=1 5=2
(appreciate: 6521 changes made)

. recode control   1=-2 2=-1 3=0 4=1 5=2
(control: 6530 changes made)

. recode innocent   1=-2 2=-1 3=0 4=1 5=2
(innocent: 6533 changes made)

. 
. * Create scale
. * rowtotal 
. egen hostile = rowtotal(innocent control appreciate), missing
(90 missing values generated)

. 
. * Recode -1 to +1 
. gen hostile_rescale = hostile / 6
(90 missing values generated)

. 
. gen hostile_rescale2 = hostile_rescale+1
(90 missing values generated)

. 
. codebook ID if hostile_rescale2!=.

-------------------------------------------------------------------------------------------------------------
ID                                                                                                         ID
-------------------------------------------------------------------------------------------------------------

                  type:  numeric (double)

                 range:  [2.198e+08,2.198e+08]        units:  1
         unique values:  2,139                    missing .:  0/6,557

                  mean:   2.2e+08
              std. dev:   5730.98

           percentiles:        10%       25%       50%       75%       90%
                           2.2e+08   2.2e+08   2.2e+08   2.2e+08   2.2e+08

. 
. gen nothostile=1 if hostile_rescale<0
(3,091 missing values generated)

. replace nothostile=0 if hostile_rescale>=0
(3,091 real changes made)

. gen hostile2=1 if hostile_rescale>0
(4,631 missing values generated)

. replace hostile2=0 if hostile_rescale<=0
(4,631 real changes made)

. 
. ** Consolidate parties into four main parties
. gen party_name="Liberal" if PartyNm=="Liberal"|PartyNm=="Country Liberals (NT)"| ///
>         PartyNm=="Liberal National Party of Queensland"
(4,665 missing values generated)

. replace party_name="National" if PartyNm=="National Party"|PartyNm=="The Nationals"
variable party_name was str7 now str8
(327 real changes made)

. replace party_name="Labor" if PartyNm=="Australian Labor Party"| ///
>         PartyNm=="Australian Labor Party (Northern Territory) Branch"|PartyNm=="Labor"
(2,169 real changes made)

. replace party_name="Greens" if PartyNm=="The Greens"|PartyNm=="The Greens (VIC)"|PartyNm=="The Greens (WA)"
(2,169 real changes made)

. 
. ** Non party identifiers
. gen nonpartisan=1 if B1==6
(5,687 missing values generated)

. replace nonpartisan=0 if nonpartisan==.&B1!=999
(5,593 real changes made)

. 
. ** Candidate party
. gen party_candidate=1 if party_name=="Liberal"|party_name=="National"
(4,338 missing values generated)

. replace party_candidate=2 if party_name=="Labor"
(2,169 real changes made)

. replace party_candidate=3 if party_name=="Greens"
(2,169 real changes made)

. label define partylab 1 "Liberal-National" 2 "Labor" 3 "Greens"

. label values party_candidate partylab

. 
. ** Respondent gender
. gen female=1 if H1==2
(3,213 missing values generated)

. replace female=0 if H1==1
(3,102 real changes made)

. label define femlab 0 "men" 1 "women"

. label values female femlab

. 
. ** Incumbent
. gen incumbent=1 if Incumbent=="Y"
(4,872 missing values generated)

. replace incumbent=0 if Incumbent=="N"
(4,872 real changes made)

. label define incumbentlab 0 "not incumbent" 1 "incumbent"

. label values incumbent incumbentlab

. 
. save "Data\Australia_data_recoded.dta", replace
file Data\Australia_data_recoded.dta saved

. 
. ** define sample
. gen fullsample=1 if vote_choice!=.&candidate_gender!=.&party_candidate!=.&incumbent!=.&distance!=.&hostile_
> rescale!=.
(382 missing values generated)

. replace fullsample=0 if fullsample!=1
(382 real changes made)

. 
. ** identify respondents with all zeros on vote choice dependent var
. clogit vote_choice i.candidate_gender if fullsample==1&sample_races==1 [pw=wt_pooled], group(ID) or
note: multiple positive outcomes within groups encountered.
note: 19 groups (57 obs) dropped because of all positive or
      all negative outcomes.

Iteration 0:   log pseudolikelihood = -1606.6326  
Iteration 1:   log pseudolikelihood = -1606.5835  
Iteration 2:   log pseudolikelihood = -1606.5835  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      4,558
                                                Wald chi2(1)      =       1.85
                                                Prob > chi2       =     0.1741
Log pseudolikelihood = -1606.5835               Pseudo R2         =     0.0013

                                         (Std. Err. adjusted for clustering on ID)
----------------------------------------------------------------------------------
                 |               Robust
     vote_choice | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
candidate_gender |
          women  |   .8902842   .0761213    -1.36   0.174     .7529203    1.052709
----------------------------------------------------------------------------------

. gen wanted=e(sample)

. tab wanted

     wanted |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      2,089       31.43       31.43
          1 |      4,558       68.57      100.00
------------+-----------------------------------
      Total |      6,647      100.00

. 
. ** Keep analytical sample
. keep if wanted==1
(2,089 observations deleted)

. 
. ** sample size
. codebook ID

-------------------------------------------------------------------------------------------------------------
ID                                                                                                         ID
-------------------------------------------------------------------------------------------------------------

                  type:  numeric (double)

                 range:  [2.198e+08,2.198e+08]        units:  1
         unique values:  1,471                    missing .:  0/4,558

                  mean:   2.2e+08
              std. dev:    5727.9

           percentiles:        10%       25%       50%       75%       90%
                           2.2e+08   2.2e+08   2.2e+08   2.2e+08   2.2e+08

. 
. ** Scale reliability for the analytical sample
. alpha innocent control appreciate

Test scale = mean(unstandardized items)

Average interitem covariance:     .7577268
Number of items in the scale:            3
Scale reliability coefficient:      0.8269

. 
. 
. ** Models
. ** Voting for female candidates - with and without controls
. quietly clogit vote_choice i.candidate_gender if fullsample==1&sample_races==1 [pw=wt_pooled], group(ID) or

. outreg using "Output\BJPS resubmission\Table 6 Australia.doc", replace se or starlevels(10 5 1)

                                     -----------------------------------
                                                           vote_choice 
                                     -----------------------------------
                                      1.candidate_gender      0.890    
                                                             (0.076)   
                                      N                       4,558    
                                     -----------------------------------
                                       * p<0.1; ** p<0.05; *** p<0.01


. margins, dydx(candidate_gender) post

Conditional marginal effects                    Number of obs     =      4,558
Model VCE    : Robust

Expression   : Pr(vote_choice|fixed effect is 0), predict(pu0)
dy/dx w.r.t. : 1.candidate_gender

----------------------------------------------------------------------------------
                 |            Delta-method
                 |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
candidate_gender |
          women  |   -.029021   .0213036    -1.36   0.173    -.0707752    .0127332
----------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. est store model_1

. quietly clogit vote_choice i.candidate_gender i.party_candidate i.incumbent c.distance ///
>         if fullsample==1&sample_races==1 [pw=wt_pooled], group(ID) or

. outreg using "Output\BJPS resubmission\Table 6 Australia.doc", merge se or starlevels(10 5 1)

                              -------------------------------------------------
                                                     vote_choice  vote_choice 
                              -------------------------------------------------
                               1.candidate_gender       0.890        1.035    
                                                       (0.076)      (0.095)   
                               2bn.party_candidate                   0.817    
                                                                   (0.090)*   
                               3.party_candidate                     1.195    
                                                                    (0.150)   
                               1.incumbent                           1.658    
                                                                  (0.219)***  
                               distance                              0.987    
                                                                  (0.004)***  
                               N                        4,558        4,558    
                              -------------------------------------------------
                                       * p<0.1; ** p<0.05; *** p<0.01


. margins, dydx(candidate_gender) post

Average marginal effects                        Number of obs     =      4,558
Model VCE    : Robust

Expression   : Pr(vote_choice|fixed effect is 0), predict(pu0)
dy/dx w.r.t. : 1.candidate_gender

----------------------------------------------------------------------------------
                 |            Delta-method
                 |      dy/dx   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
candidate_gender |
          women  |   .0082965   .0221297     0.37   0.708     -.035077      .05167
----------------------------------------------------------------------------------
Note: dy/dx for factor levels is the discrete change from the base level.

. est store model_2

. coefplot (model_1, label("M1 - Without controls")) (model_2, label("M2 - With controls")), title("Australia
> ") ///
>         ytitle("AME voting for female candidate") ylabel("") xline(0) saving("Output\BJPS resubmission\Aust
> ralia_table6", replace)
(file Output\BJPS resubmission\Australia_table6.gph saved)

. 
. ** Voting for female candidates by hostile sexism (controlling for sexism*party)
. 
. set more off

. clogit vote_choice i.candidate_gender##c.hostile_rescale2 i.party_candidate i.incumbent c.distance ///
>         if fullsample==1&sample_races==1 [pw=wt_pooled], group(ID) or
note: multiple positive outcomes within groups encountered.
note: hostile_rescale2 omitted because of no within-group variance.

Iteration 0:   log pseudolikelihood = -1538.6883  
Iteration 1:   log pseudolikelihood = -1535.2887  
Iteration 2:   log pseudolikelihood = -1535.2847  
Iteration 3:   log pseudolikelihood = -1535.2847  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      4,558
                                                Wald chi2(6)      =      49.72
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -1535.2847               Pseudo R2         =     0.0456

                                                            (Std. Err. adjusted for clustering on ID)
-----------------------------------------------------------------------------------------------------
                                    |               Robust
                        vote_choice | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------------+----------------------------------------------------------------
                   candidate_gender |
                             women  |    1.20491   .2040601     1.10   0.271     .8645646    1.679235
                   hostile_rescale2 |          1  (omitted)
                                    |
candidate_gender#c.hostile_rescale2 |
                             women  |   .8267223   .1465617    -1.07   0.283     .5840611    1.170203
                                    |
                    party_candidate |
                             Labor  |    .821858   .0911709    -1.77   0.077     .6612576    1.021463
                            Greens  |   1.209206   .1523331     1.51   0.132     .9446429    1.547863
                                    |
                          incumbent |
                         incumbent  |   1.666358   .2199582     3.87   0.000     1.286501    2.158372
                           distance |   .9866347   .0044575    -2.98   0.003     .9779367      .99541
-----------------------------------------------------------------------------------------------------

. outreg using "Output\BJPS resubmission\Table 7 Australia.doc", replace se or ctitles("", " c. gender*sexism
> ") starlevels(10 5 1)

                        ------------------------------------------------------------
                                                                  c. gender*sexism 
                        ------------------------------------------------------------
                         1.candidate_gender                            1.205       
                                                                      (0.204)      
                         1.candidate_gender#c.hostile_rescale2         0.827       
                                                                      (0.147)      
                         2bn.party_candidate                           0.822       
                                                                     (0.091)*      
                         3.party_candidate                             1.209       
                                                                      (0.152)      
                         1.incumbent                                   1.666       
                                                                    (0.220)***     
                         distance                                      0.987       
                                                                    (0.004)***     
                         N                                             4,558       
                        ------------------------------------------------------------
                                       * p<0.1; ** p<0.05; *** p<0.01


. clogit vote_choice i.candidate_gender##c.hostile_rescale2 i.party_candidate##c.hostile_rescale2 i.incumbent
>  c.distance ///
>         if fullsample==1&sample_races==1 [pw=wt_pooled], group(ID) or
note: hostile_rescale2 omitted because of collinearity
note: multiple positive outcomes within groups encountered.
note: hostile_rescale2 omitted because of no within-group variance.

Iteration 0:   log pseudolikelihood = -1505.5739  
Iteration 1:   log pseudolikelihood = -1487.9368  
Iteration 2:   log pseudolikelihood = -1487.9042  
Iteration 3:   log pseudolikelihood = -1487.9042  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      4,558
                                                Wald chi2(8)      =      74.48
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -1487.9042               Pseudo R2         =     0.0751

                                                            (Std. Err. adjusted for clustering on ID)
-----------------------------------------------------------------------------------------------------
                                    |               Robust
                        vote_choice | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------------+----------------------------------------------------------------
                   candidate_gender |
                             women  |   .9864758   .1667535    -0.08   0.936     .7082713    1.373957
                   hostile_rescale2 |          1  (omitted)
                                    |
candidate_gender#c.hostile_rescale2 |
                             women  |   1.128545   .2122417     0.64   0.520     .7806139    1.631554
                                    |
                    party_candidate |
                             Labor  |   2.207983   .4389573     3.98   0.000     1.495454    3.260007
                            Greens  |   2.970025   .6272269     5.15   0.000     1.963353    4.492848
                                    |
                   hostile_rescale2 |          1  (omitted)
                                    |
 party_candidate#c.hostile_rescale2 |
                             Labor  |   .2806799   .0633523    -5.63   0.000     .1803377    .4368538
                            Greens  |   .3088678   .0676118    -5.37   0.000     .2011142    .4743539
                                    |
                          incumbent |
                         incumbent  |   1.660267   .2206411     3.81   0.000      1.27955    2.154261
                           distance |   .9869809   .0045519    -2.84   0.004     .9780995    .9959429
-----------------------------------------------------------------------------------------------------

. outreg using "Output\BJPS resubmission\Table 7 Australia.doc", merge se or ctitles("", " c. gender*sexism")
>  starlevels(10 5 1)

              --------------------------------------------------------------------------------
                                                         c. gender*sexism   c. gender*sexism 
              --------------------------------------------------------------------------------
               1.candidate_gender                             1.205              0.986       
                                                             (0.204)            (0.167)      
               1.candidate_gender#c.hostile_rescale2          0.827              1.129       
                                                             (0.147)            (0.212)      
               2bn.party_candidate                            0.822              2.208       
                                                            (0.091)*          (0.439)***     
               3.party_candidate                              1.209              2.970       
                                                             (0.152)          (0.627)***     
               1.incumbent                                    1.666              1.660       
                                                           (0.220)***         (0.221)***     
               distance                                       0.987              0.987       
                                                           (0.004)***         (0.005)***     
               2bn.party_candidate#c.hostile_rescale2                            0.281       
                                                                              (0.063)***     
               3.party_candidate#c.hostile_rescale2                              0.309       
                                                                              (0.068)***     
               N                                              4,558              4,558       
              --------------------------------------------------------------------------------
                                       * p<0.1; ** p<0.05; *** p<0.01


. 
. ** Robustness checks
. * For women and men
. 
. clogit vote_choice i.candidate_gender##c.hostile_rescale2 i.party_candidate##c.hostile_rescale2 i.incumbent
>  c.distance ///
>         if fullsample==1&female==1&sample_races==1 [pw=wt_pooled], group(ID) or
note: hostile_rescale2 omitted because of collinearity
note: multiple positive outcomes within groups encountered.
note: hostile_rescale2 omitted because of no within-group variance.

Iteration 0:   log pseudolikelihood = -748.94819  
Iteration 1:   log pseudolikelihood = -742.23426  
Iteration 2:   log pseudolikelihood = -742.22782  
Iteration 3:   log pseudolikelihood = -742.22782  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      2,344
                                                Wald chi2(8)      =      31.07
                                                Prob > chi2       =     0.0001
Log pseudolikelihood = -742.22782               Pseudo R2         =     0.0557

                                                            (Std. Err. adjusted for clustering on ID)
-----------------------------------------------------------------------------------------------------
                                    |               Robust
                        vote_choice | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------------+----------------------------------------------------------------
                   candidate_gender |
                             women  |   .9458871   .2179674    -0.24   0.809      .602131    1.485893
                   hostile_rescale2 |          1  (omitted)
                                    |
candidate_gender#c.hostile_rescale2 |
                             women  |   1.116464   .3411704     0.36   0.718     .6133853    2.032151
                                    |
                    party_candidate |
                             Labor  |   2.323878   .5841324     3.35   0.001     1.419889    3.803402
                            Greens  |   2.553376   .6927381     3.46   0.001     1.500311    4.345584
                                    |
                   hostile_rescale2 |          1  (omitted)
                                    |
 party_candidate#c.hostile_rescale2 |
                             Labor  |   .2677094   .0859367    -4.11   0.000     .1426992    .5022336
                            Greens  |   .3218718   .1028985    -3.55   0.000     .1720142    .6022845
                                    |
                          incumbent |
                         incumbent  |   1.574185   .2858324     2.50   0.012     1.102806    2.247049
                           distance |   .9915164   .0064544    -1.31   0.191     .9789464    1.004248
-----------------------------------------------------------------------------------------------------

. outreg using "Output\BJPS resubmission\Robustness checks\gender Australia.doc", replace se or ctitles("", "
> women") starlevels(10 5 1)

                           ------------------------------------------------------
                                                                       women    
                           ------------------------------------------------------
                            1.candidate_gender                         0.946    
                                                                      (0.218)   
                            1.candidate_gender#c.hostile_rescale2      1.116    
                                                                      (0.341)   
                            2bn.party_candidate                        2.324    
                                                                     (0.584)*** 
                            3.party_candidate                          2.553    
                                                                     (0.693)*** 
                            2bn.party_candidate#c.hostile_rescale2     0.268    
                                                                     (0.086)*** 
                            3.party_candidate#c.hostile_rescale2       0.322    
                                                                     (0.103)*** 
                            1.incumbent                                1.574    
                                                                     (0.286)**  
                            distance                                   0.992    
                                                                      (0.006)   
                            N                                          2,344    
                           ------------------------------------------------------
                                       * p<0.1; ** p<0.05; *** p<0.01


. clogit vote_choice i.candidate_gender##c.hostile_rescale2 i.party_candidate##c.hostile_rescale2 i.incumbent
>  c.distance ///
>         if fullsample==1&female==0&sample_races==1 [pw=wt_pooled], group(ID) or
note: hostile_rescale2 omitted because of collinearity
note: multiple positive outcomes within groups encountered.
note: hostile_rescale2 omitted because of no within-group variance.

Iteration 0:   log pseudolikelihood = -745.63777  
Iteration 1:   log pseudolikelihood = -735.10673  
Iteration 2:   log pseudolikelihood = -735.03501  
Iteration 3:   log pseudolikelihood =   -735.035  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      2,166
                                                Wald chi2(8)      =      43.21
                                                Prob > chi2       =     0.0000
Log pseudolikelihood =   -735.035               Pseudo R2         =     0.0983

                                                            (Std. Err. adjusted for clustering on ID)
-----------------------------------------------------------------------------------------------------
                                    |               Robust
                        vote_choice | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------------+----------------------------------------------------------------
                   candidate_gender |
                             women  |   1.051394   .2693776     0.20   0.845     .6363255    1.737208
                   hostile_rescale2 |          1  (omitted)
                                    |
candidate_gender#c.hostile_rescale2 |
                             women  |   1.120734   .2835694     0.45   0.652      .682544     1.84024
                                    |
                    party_candidate |
                             Labor  |   2.050419   .6732009     2.19   0.029     1.077391    3.902224
                            Greens  |    3.49992   1.188773     3.69   0.000     1.798628    6.810435
                                    |
                   hostile_rescale2 |          1  (omitted)
                                    |
 party_candidate#c.hostile_rescale2 |
                             Labor  |   .3041847   .1034759    -3.50   0.000     .1561638    .5925083
                            Greens  |   .3003128   .0981175    -3.68   0.000     .1582965    .5697397
                                    |
                          incumbent |
                         incumbent  |   1.791111   .3540253     2.95   0.003     1.215841    2.638568
                           distance |   .9825421   .0064271    -2.69   0.007     .9700256    .9952202
-----------------------------------------------------------------------------------------------------

. outreg using "Output\BJPS resubmission\Robustness checks\gender Australia.doc", merge se or ctitles("", "me
> n") starlevels(10 5 1)

                     ------------------------------------------------------------------
                                                                 women        men     
                     ------------------------------------------------------------------
                      1.candidate_gender                         0.946       1.051    
                                                                (0.218)     (0.269)   
                      1.candidate_gender#c.hostile_rescale2      1.116       1.121    
                                                                (0.341)     (0.284)   
                      2bn.party_candidate                        2.324       2.050    
                                                               (0.584)***  (0.673)**  
                      3.party_candidate                          2.553       3.500    
                                                               (0.693)***  (1.189)*** 
                      2bn.party_candidate#c.hostile_rescale2     0.268       0.304    
                                                               (0.086)***  (0.103)*** 
                      3.party_candidate#c.hostile_rescale2       0.322       0.300    
                                                               (0.103)***  (0.098)*** 
                      1.incumbent                                1.574       1.791    
                                                               (0.286)**   (0.354)*** 
                      distance                                   0.992       0.983    
                                                                (0.006)    (0.006)*** 
                      N                                          2,344       2,166    
                     ------------------------------------------------------------------
                                       * p<0.1; ** p<0.05; *** p<0.01


. 
. ** For partisans and non-partisans
. clogit vote_choice i.candidate_gender##c.hostile_rescale2 i.party_candidate##c.hostile_rescale2 i.incumbent
>  c.distance ///
>         if fullsample==1&sample_races==1&nonpartisan==0 [pw=wt_pooled], group(ID) or
note: hostile_rescale2 omitted because of collinearity
note: multiple positive outcomes within groups encountered.
note: hostile_rescale2 omitted because of no within-group variance.

Iteration 0:   log pseudolikelihood = -1244.3062  
Iteration 1:   log pseudolikelihood = -1226.4832  
Iteration 2:   log pseudolikelihood = -1226.4254  
Iteration 3:   log pseudolikelihood = -1226.4254  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      3,976
                                                Wald chi2(8)      =      72.68
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -1226.4254               Pseudo R2         =     0.0948

                                                            (Std. Err. adjusted for clustering on ID)
-----------------------------------------------------------------------------------------------------
                                    |               Robust
                        vote_choice | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------------+----------------------------------------------------------------
                   candidate_gender |
                             women  |   .9713013   .1775904    -0.16   0.873     .6787694    1.389907
                   hostile_rescale2 |          1  (omitted)
                                    |
candidate_gender#c.hostile_rescale2 |
                             women  |   1.154202   .2401181     0.69   0.491     .7677139    1.735259
                                    |
                    party_candidate |
                             Labor  |   2.275377   .4847909     3.86   0.000     1.498638    3.454697
                            Greens  |   2.991681    .690836     4.75   0.000      1.90264    4.704071
                                    |
                   hostile_rescale2 |          1  (omitted)
                                    |
 party_candidate#c.hostile_rescale2 |
                             Labor  |    .241344   .0599741    -5.72   0.000     .1482902      .39279
                            Greens  |   .2984994   .0723125    -4.99   0.000     .1856681     .479899
                                    |
                          incumbent |
                         incumbent  |   1.864167   .2643424     4.39   0.000     1.411832    2.461426
                           distance |   .9865038   .0048955    -2.74   0.006     .9769553    .9961456
-----------------------------------------------------------------------------------------------------

. outreg using "Output\BJPS resubmission\Robustness checks\partisanship Australia.doc", replace se or ctitles
> ("", " c. gender*sexism") starlevels(10 5 1)

                        -------------------------------------------------------------
                                                                   c. gender*sexism 
                        -------------------------------------------------------------
                         1.candidate_gender                             0.971       
                                                                       (0.178)      
                         1.candidate_gender#c.hostile_rescale2          1.154       
                                                                       (0.240)      
                         2bn.party_candidate                            2.275       
                                                                     (0.485)***     
                         3.party_candidate                              2.992       
                                                                     (0.691)***     
                         2bn.party_candidate#c.hostile_rescale2         0.241       
                                                                     (0.060)***     
                         3.party_candidate#c.hostile_rescale2           0.298       
                                                                     (0.072)***     
                         1.incumbent                                    1.864       
                                                                     (0.264)***     
                         distance                                       0.987       
                                                                     (0.005)***     
                         N                                              3,976       
                        -------------------------------------------------------------
                                       * p<0.1; ** p<0.05; *** p<0.01


. clogit vote_choice i.candidate_gender##c.hostile_rescale2 i.party_candidate##c.hostile_rescale2 i.incumbent
>  c.distance ///
>         if fullsample==1&sample_races==1&nonpartisan==1 [pw=wt_pooled], group(ID) or
note: hostile_rescale2 omitted because of collinearity
note: multiple positive outcomes within groups encountered.
note: hostile_rescale2 omitted because of no within-group variance.

Iteration 0:   log pseudolikelihood = -241.83731  
Iteration 1:   log pseudolikelihood = -241.13255  
Iteration 2:   log pseudolikelihood = -241.13213  
Iteration 3:   log pseudolikelihood = -241.13213  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =        554
                                                Wald chi2(8)      =       6.07
                                                Prob > chi2       =     0.6389
Log pseudolikelihood = -241.13213               Pseudo R2         =     0.0263

                                                            (Std. Err. adjusted for clustering on ID)
-----------------------------------------------------------------------------------------------------
                                    |               Robust
                        vote_choice | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------------+----------------------------------------------------------------
                   candidate_gender |
                             women  |   1.024983   .4882711     0.05   0.959     .4029301    2.607375
                   hostile_rescale2 |          1  (omitted)
                                    |
candidate_gender#c.hostile_rescale2 |
                             women  |   1.060076   .4920757     0.13   0.900      .426794    2.633028
                                    |
                    party_candidate |
                             Labor  |   1.530083   .8545425     0.76   0.446     .5120637    4.571999
                            Greens  |   2.711606   1.505564     1.80   0.072     .9133015    8.050799
                                    |
                   hostile_rescale2 |          1  (omitted)
                                    |
 party_candidate#c.hostile_rescale2 |
                             Labor  |   .6850526   .3967959    -0.65   0.514     .2201361     2.13185
                            Greens  |   .3279033   .1906953    -1.92   0.055     .1048869    1.025109
                                    |
                          incumbent |
                         incumbent  |   .9852127   .3525405    -0.04   0.967     .4885882     1.98663
                           distance |   .9899835   .0116433    -0.86   0.392     .9674241    1.013069
-----------------------------------------------------------------------------------------------------

. outreg using "Output\BJPS resubmission\Robustness checks\partisanship Australia.doc", merge se or ctitles("
> ", " c. gender*sexism") starlevels(10 5 1)

              --------------------------------------------------------------------------------
                                                         c. gender*sexism   c. gender*sexism 
              --------------------------------------------------------------------------------
               1.candidate_gender                             0.971              1.025       
                                                             (0.178)            (0.488)      
               1.candidate_gender#c.hostile_rescale2          1.154              1.060       
                                                             (0.240)            (0.492)      
               2bn.party_candidate                            2.275              1.530       
                                                           (0.485)***           (0.855)      
               3.party_candidate                              2.992              2.712       
                                                           (0.691)***          (1.506)*      
               2bn.party_candidate#c.hostile_rescale2         0.241              0.685       
                                                           (0.060)***           (0.397)      
               3.party_candidate#c.hostile_rescale2           0.298              0.328       
                                                           (0.072)***          (0.191)*      
               1.incumbent                                    1.864              0.985       
                                                           (0.264)***           (0.353)      
               distance                                       0.987              0.990       
                                                           (0.005)***           (0.012)      
               N                                              3,976               554        
              --------------------------------------------------------------------------------
                                       * p<0.1; ** p<0.05; *** p<0.01


. 
. ** By party
. clogit vote_choice i.candidate_gender##c.hostile_rescale2 i.party_candidate##c.hostile_rescale2 i.incumbent
>  c.distance ///
>         if fullsample==1&sample_races==1&B1==1|B1==3 [pw=wt_pooled], group(ID) or
note: hostile_rescale2 omitted because of collinearity
note: multiple positive outcomes within groups encountered.
note: hostile_rescale2 omitted because of no within-group variance.

Iteration 0:   log pseudolikelihood = -365.24726  
Iteration 1:   log pseudolikelihood = -358.77824  
Iteration 2:   log pseudolikelihood =  -358.3718  
Iteration 3:   log pseudolikelihood =  -358.3706  
Iteration 4:   log pseudolikelihood =  -358.3706  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      1,959
                                                Wald chi2(8)      =     116.02
                                                Prob > chi2       =     0.0000
Log pseudolikelihood =  -358.3706               Pseudo R2         =     0.4221

                                                            (Std. Err. adjusted for clustering on ID)
-----------------------------------------------------------------------------------------------------
                                    |               Robust
                        vote_choice | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------------+----------------------------------------------------------------
                   candidate_gender |
                             women  |   1.572259   .5862794     1.21   0.225     .7570418    3.265341
                   hostile_rescale2 |          1  (omitted)
                                    |
candidate_gender#c.hostile_rescale2 |
                             women  |   .8048337   .2878662    -0.61   0.544     .3992597    1.622396
                                    |
                    party_candidate |
                             Labor  |   .1681256   .0983885    -3.05   0.002     .0533954    .5293761
                            Greens  |     .20993   .1234137    -2.66   0.008     .0663239    .6644757
                                    |
                   hostile_rescale2 |          1  (omitted)
                                    |
 party_candidate#c.hostile_rescale2 |
                             Labor  |   .3809569   .2440686    -1.51   0.132     .1085262    1.337264
                            Greens  |   .7258911   .4316804    -0.54   0.590     .2262925    2.328481
                                    |
                          incumbent |
                         incumbent  |   1.078631   .2932092     0.28   0.781     .6331214    1.837633
                           distance |   .9730759   .0082489    -3.22   0.001     .9570419    .9893786
-----------------------------------------------------------------------------------------------------

. outreg using "Output\BJPS resubmission\Robustness checks\party Australia.doc", replace se or ctitles("", " 
> c. Liberal-National identifiers") starlevels(10 5 1)

                ----------------------------------------------------------------------------
                                                           c. Liberal-National identifiers 
                ----------------------------------------------------------------------------
                 1.candidate_gender                                    1.572               
                                                                      (0.586)              
                 1.candidate_gender#c.hostile_rescale2                 0.805               
                                                                      (0.288)              
                 2bn.party_candidate                                   0.168               
                                                                     (0.098)***            
                 3.party_candidate                                     0.210               
                                                                     (0.123)***            
                 2bn.party_candidate#c.hostile_rescale2                0.381               
                                                                      (0.244)              
                 3.party_candidate#c.hostile_rescale2                  0.726               
                                                                      (0.432)              
                 1.incumbent                                           1.079               
                                                                      (0.293)              
                 distance                                              0.973               
                                                                     (0.008)***            
                 N                                                     1,959               
                ----------------------------------------------------------------------------
                                       * p<0.1; ** p<0.05; *** p<0.01


. clogit vote_choice i.candidate_gender##c.hostile_rescale2 i.party_candidate##c.hostile_rescale2 i.incumbent
>  c.distance ///
>         if fullsample==1&sample_races==1&B1==2 [pw=wt_pooled], group(ID) or
note: hostile_rescale2 omitted because of collinearity
note: multiple positive outcomes within groups encountered.
note: hostile_rescale2 omitted because of no within-group variance.

Iteration 0:   log pseudolikelihood = -342.93352  
Iteration 1:   log pseudolikelihood = -333.81103  
Iteration 2:   log pseudolikelihood = -332.22333  
Iteration 3:   log pseudolikelihood = -332.22218  
Iteration 4:   log pseudolikelihood = -332.22218  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      1,477
                                                Wald chi2(8)      =     108.80
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -332.22218               Pseudo R2         =     0.3233

                                                            (Std. Err. adjusted for clustering on ID)
-----------------------------------------------------------------------------------------------------
                                    |               Robust
                        vote_choice | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------------+----------------------------------------------------------------
                   candidate_gender |
                             women  |      1.438   .4577992     1.14   0.254     .7704989    2.683774
                   hostile_rescale2 |          1  (omitted)
                                    |
candidate_gender#c.hostile_rescale2 |
                             women  |   .9707882   .3763868    -0.08   0.939     .4540492    2.075611
                                    |
                    party_candidate |
                             Labor  |   14.81991    8.94108     4.47   0.000     4.542581    48.34912
                            Greens  |   3.103579   1.170728     3.00   0.003     1.481746    6.500577
                                    |
                   hostile_rescale2 |          1  (omitted)
                                    |
 party_candidate#c.hostile_rescale2 |
                             Labor  |   .5659404   .3753152    -0.86   0.391      .154269    2.076169
                            Greens  |   .7890603   .2944469    -0.63   0.526     .3797296     1.63963
                                    |
                          incumbent |
                         incumbent  |   2.039454   .6191654     2.35   0.019     1.124849    3.697714
                           distance |   .9846971    .008935    -1.70   0.089     .9673396    1.002366
-----------------------------------------------------------------------------------------------------

. outreg using "Output\BJPS resubmission\Robustness checks\party Australia.doc", merge se or ctitles("", " c.
>  Labor identifiers") starlevels(10 5 1)

     ---------------------------------------------------------------------------------------------------
                                                c. Liberal-National identifiers   c. Labor identifiers 
     ---------------------------------------------------------------------------------------------------
      1.candidate_gender                                    1.572                        1.438         
                                                           (0.586)                      (0.458)        
      1.candidate_gender#c.hostile_rescale2                 0.805                        0.971         
                                                           (0.288)                      (0.376)        
      2bn.party_candidate                                   0.168                       14.820         
                                                          (0.098)***                  (8.941)***       
      3.party_candidate                                     0.210                        3.104         
                                                          (0.123)***                  (1.171)***       
      2bn.party_candidate#c.hostile_rescale2                0.381                        0.566         
                                                           (0.244)                      (0.375)        
      3.party_candidate#c.hostile_rescale2                  0.726                        0.789         
                                                           (0.432)                      (0.294)        
      1.incumbent                                           1.079                        2.039         
                                                           (0.293)                     (0.619)**       
      distance                                              0.973                        0.985         
                                                          (0.008)***                   (0.009)*        
      N                                                     1,959                        1,477         
     ---------------------------------------------------------------------------------------------------
                                       * p<0.1; ** p<0.05; *** p<0.01


. 
. ** Using "women seek to gain power by getting control over men" (consistent across countries)
. clogit vote_choice i.candidate_gender##c.control i.party_candidate##c.hostile_rescale2 i.incumbent c.distan
> ce ///
>         if fullsample==1&sample_races==1 [pw=wt_pooled], group(ID) or
note: multiple positive outcomes within groups encountered.
note: control omitted because of no within-group variance.
note: hostile_rescale2 omitted because of no within-group variance.

Iteration 0:   log pseudolikelihood = -1504.6934  
Iteration 1:   log pseudolikelihood =  -1487.255  
Iteration 2:   log pseudolikelihood = -1487.2221  
Iteration 3:   log pseudolikelihood = -1487.2221  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      4,549
                                                Wald chi2(8)      =      74.10
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -1487.2221               Pseudo R2         =     0.0750

                                                           (Std. Err. adjusted for clustering on ID)
----------------------------------------------------------------------------------------------------
                                   |               Robust
                       vote_choice | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------------------------+----------------------------------------------------------------
                  candidate_gender |
                            women  |   1.104242   .1249165     0.88   0.381     .8846531    1.378339
                           control |          1  (omitted)
                                   |
        candidate_gender#c.control |
                            women  |   1.030439   .0852789     0.36   0.717     .8761469    1.211902
                                   |
                   party_candidate |
                            Labor  |   2.182087   .4342113     3.92   0.000     1.477381    3.222937
                           Greens  |   2.951395   .6240328     5.12   0.000     1.950079    4.466861
                                   |
                  hostile_rescale2 |          1  (omitted)
                                   |
party_candidate#c.hostile_rescale2 |
                            Labor  |   .2857145    .064417    -5.56   0.000     .1836626    .4444713
                           Greens  |   .3132735   .0684539    -5.31   0.000     .2041391    .4807521
                                   |
                         incumbent |
                        incumbent  |   1.663466   .2217052     3.82   0.000     1.281052    2.160038
                          distance |   .9870102   .0045585    -2.83   0.005     .9781159    .9959853
----------------------------------------------------------------------------------------------------

. outreg using "Output\BJPS resubmission\Robustness checks\control item Australia.doc", replace or

                           -------------------------------------------------------
                                                                     vote_choice 
                           -------------------------------------------------------
                            1.candidate_gender                          1.104    
                                                                       (0.88)    
                            1.candidate_gender#c.control                1.030    
                                                                       (0.36)    
                            2bn.party_candidate                         2.182    
                                                                      (3.92)**   
                            3.party_candidate                           2.951    
                                                                      (5.12)**   
                            2bn.party_candidate#c.hostile_rescale2      0.286    
                                                                      (5.56)**   
                            3.party_candidate#c.hostile_rescale2        0.313    
                                                                      (5.31)**   
                            1.incumbent                                 1.663    
                                                                      (3.82)**   
                            distance                                    0.987    
                                                                      (2.83)**   
                            N                                           4,549    
                           -------------------------------------------------------
                                             * p<0.05; ** p<0.01


. 
. * Vote share at previous election as a control
. ** Correlation between distance from winner and vote share = -0.8616
. clogit vote_choice i.candidate_gender i.party_candidate i.incumbent c.voteshare2016 ///
>         if fullsample==1&sample_races==1 [pw=wt_pooled], group(ID) or
note: multiple positive outcomes within groups encountered.

Iteration 0:   log pseudolikelihood = -1516.9375  
Iteration 1:   log pseudolikelihood = -1512.7566  
Iteration 2:   log pseudolikelihood = -1512.7529  
Iteration 3:   log pseudolikelihood = -1512.7529  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      4,558
                                                Wald chi2(5)      =      65.62
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -1512.7529               Pseudo R2         =     0.0596

                                         (Std. Err. adjusted for clustering on ID)
----------------------------------------------------------------------------------
                 |               Robust
     vote_choice | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
-----------------+----------------------------------------------------------------
candidate_gender |
          women  |   1.038695   .0946571     0.42   0.677     .8687952     1.24182
                 |
 party_candidate |
          Labor  |   .6511774   .0706567    -3.95   0.000     .5264277    .8054896
         Greens  |   .5549402   .0845181    -3.87   0.000     .4117248    .7479719
                 |
       incumbent |
      incumbent  |   2.763359   .3787908     7.42   0.000     2.112311    3.615071
   voteshare2016 |   .9767345   .0044409    -5.18   0.000     .9680692    .9854773
----------------------------------------------------------------------------------

. outreg using "Output\BJPS resubmission\Robustness checks\vote share control Australia.doc", replace se or s
> tarlevels(10 5 1)

                                    ------------------------------------
                                                           vote_choice 
                                    ------------------------------------
                                     1.candidate_gender       1.039    
                                                             (0.095)   
                                     2bn.party_candidate      0.651    
                                                           (0.071)***  
                                     3.party_candidate        0.555    
                                                           (0.085)***  
                                     1.incumbent              2.763    
                                                           (0.379)***  
                                     voteshare2016            0.977    
                                                           (0.004)***  
                                     N                        4,558    
                                    ------------------------------------
                                       * p<0.1; ** p<0.05; *** p<0.01


. clogit vote_choice i.candidate_gender##c.hostile_rescale2 i.party_candidate i.incumbent c.voteshare2016 ///
>         if fullsample==1&sample_races==1 [pw=wt_pooled], group(ID) or
note: multiple positive outcomes within groups encountered.
note: hostile_rescale2 omitted because of no within-group variance.

Iteration 0:   log pseudolikelihood = -1515.3962  
Iteration 1:   log pseudolikelihood =  -1511.035  
Iteration 2:   log pseudolikelihood = -1511.0311  
Iteration 3:   log pseudolikelihood = -1511.0311  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      4,558
                                                Wald chi2(6)      =      67.64
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -1511.0311               Pseudo R2         =     0.0607

                                                            (Std. Err. adjusted for clustering on ID)
-----------------------------------------------------------------------------------------------------
                                    |               Robust
                        vote_choice | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------------+----------------------------------------------------------------
                   candidate_gender |
                             women  |    1.23983   .2073556     1.29   0.199     .8933109    1.720764
                   hostile_rescale2 |          1  (omitted)
                                    |
candidate_gender#c.hostile_rescale2 |
                             women  |   .8017432    .142135    -1.25   0.213     .5664117     1.13485
                                    |
                    party_candidate |
                             Labor  |   .6551175   .0715012    -3.88   0.000     .5289528    .8113747
                            Greens  |   .5622281   .0858911    -3.77   0.000     .4167496    .7584902
                                    |
                          incumbent |
                         incumbent  |   2.777329   .3804728     7.46   0.000     2.123338    3.632749
                      voteshare2016 |    .976656   .0044217    -5.22   0.000      .968028     .985361
-----------------------------------------------------------------------------------------------------

. outreg using "Output\BJPS resubmission\Robustness checks\vote share control Australia.doc", merge se or sta
> rlevels(10 5 1)

                     -------------------------------------------------------------------
                                                              vote_choice  vote_choice 
                     -------------------------------------------------------------------
                      1.candidate_gender                         1.039        1.240    
                                                                (0.095)      (0.207)   
                      2bn.party_candidate                        0.651        0.655    
                                                              (0.071)***   (0.072)***  
                      3.party_candidate                          0.555        0.562    
                                                              (0.085)***   (0.086)***  
                      1.incumbent                                2.763        2.777    
                                                              (0.379)***   (0.380)***  
                      voteshare2016                              0.977        0.977    
                                                              (0.004)***   (0.004)***  
                      1.candidate_gender#c.hostile_rescale2                   0.802    
                                                                             (0.142)   
                      N                                          4,558        4,558    
                     -------------------------------------------------------------------
                                       * p<0.1; ** p<0.05; *** p<0.01


. clogit vote_choice i.candidate_gender##c.hostile_rescale2 i.party_candidate##c.hostile_rescale2 i.incumbent
>  c.voteshare2016 ///
>         if fullsample==1&sample_races==1 [pw=wt_pooled], group(ID) or
note: hostile_rescale2 omitted because of collinearity
note: multiple positive outcomes within groups encountered.
note: hostile_rescale2 omitted because of no within-group variance.

Iteration 0:   log pseudolikelihood = -1479.6563  
Iteration 1:   log pseudolikelihood = -1460.0387  
Iteration 2:   log pseudolikelihood =  -1459.997  
Iteration 3:   log pseudolikelihood =  -1459.997  

Conditional (fixed-effects) logistic regression

                                                Number of obs     =      4,558
                                                Wald chi2(8)      =      84.28
                                                Prob > chi2       =     0.0000
Log pseudolikelihood =  -1459.997               Pseudo R2         =     0.0924

                                                            (Std. Err. adjusted for clustering on ID)
-----------------------------------------------------------------------------------------------------
                                    |               Robust
                        vote_choice | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------------+----------------------------------------------------------------
                   candidate_gender |
                             women  |   1.016627   .1698573     0.10   0.921      .732729    1.410522
                   hostile_rescale2 |          1  (omitted)
                                    |
candidate_gender#c.hostile_rescale2 |
                             women  |   1.088718   .2031597     0.46   0.649     .7552267    1.569472
                                    |
                    party_candidate |
                             Labor  |   1.876022   .3679913     3.21   0.001     1.277234    2.755532
                            Greens  |   1.372861   .3057505     1.42   0.155     .8872683    2.124213
                                    |
                   hostile_rescale2 |          1  (omitted)
                                    |
 party_candidate#c.hostile_rescale2 |
                             Labor  |   .2572816   .0595178    -5.87   0.000     .1634924    .4048741
                            Greens  |   .3013881   .0695659    -5.20   0.000     .1917137    .4738044
                                    |
                          incumbent |
                         incumbent  |   2.841466   .4060075     7.31   0.000     2.147421    3.759827
                      voteshare2016 |   .9752807   .0045946    -5.31   0.000     .9663169    .9843277
-----------------------------------------------------------------------------------------------------

. outreg using "Output\BJPS resubmission\Robustness checks\vote share control Australia.doc", merge se or sta
> rlevels(10 5 1)

              ---------------------------------------------------------------------------------
                                                        vote_choice  vote_choice  vote_choice 
              ---------------------------------------------------------------------------------
               1.candidate_gender                          1.039        1.240        1.017    
                                                          (0.095)      (0.207)      (0.170)   
               2bn.party_candidate                         0.651        0.655        1.876    
                                                        (0.071)***   (0.072)***   (0.368)***  
               3.party_candidate                           0.555        0.562        1.373    
                                                        (0.085)***   (0.086)***     (0.306)   
               1.incumbent                                 2.763        2.777        2.841    
                                                        (0.379)***   (0.380)***   (0.406)***  
               voteshare2016                               0.977        0.977        0.975    
                                                        (0.004)***   (0.004)***   (0.005)***  
               1.candidate_gender#c.hostile_rescale2                    0.802        1.089    
                                                                       (0.142)      (0.203)   
               2bn.party_candidate#c.hostile_rescale2                                0.257    
                                                                                  (0.060)***  
               3.party_candidate#c.hostile_rescale2                                  0.301    
                                                                                  (0.070)***  
               N                                           4,558        4,558        4,558    
              ---------------------------------------------------------------------------------
                                       * p<0.1; ** p<0.05; *** p<0.01


. 
. ** Table 3 for analytical sample only
. collapse hostile2 nothostile [iw=wt_pooled], by(ID)

. 
. tab nothostile

     (mean) |
 nothostile |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |        651       44.26       44.26
          1 |        820       55.74      100.00
------------+-----------------------------------
      Total |      1,471      100.00

. tab hostile2

     (mean) |
   hostile2 |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 |      1,047       71.18       71.18
          1 |        424       28.82      100.00
------------+-----------------------------------
      Total |      1,471      100.00

. 
. ** TURNOUT
. 
. use "Data\Australia_data_recoded.dta", clear

. 
. ** 95% turnout in the weighted sample
. ** Abstainers (includes informal votes)
. gen abstain=1 if B9_1==6
(6,494 missing values generated)

. replace abstain=0 if B9_1!=6&B9_1!=999
(6,328 real changes made)

. 
. tab abstain [iw=wt_pooled]

    abstain |      Freq.     Percent        Cum.
------------+-----------------------------------
          0 | 5,972.8571       94.84       94.84
          1 | 325.021329        5.16      100.00
------------+-----------------------------------
      Total | 6,297.8785      100.00

. 
. ** Co-partisan
. gen copartisan=1 if party_candidate==1&B1==1|party_candidate==1&B1==3|party_candidate==2&B1==2| ///
>         party_candidate==4&B1==4
(5,019 missing values generated)

. replace copartisan=0 if copartisan==.
(5,019 real changes made)

. 
. drop if copartisan==0
(5,019 observations deleted)

. 
. ** Age
. gen age=AGE

. replace age=. if age==999
(37 real changes made, 37 to missing)

. 
. ** Education
. * AES provides age left secondary school and post-school qualifications
. gen educ=0 if G3==1
(1,315 missing values generated)

. replace educ=1 if G3==2|G3==3
(624 real changes made)

. replace educ=2 if G3>=4&G3<=7
(635 real changes made)

. label define educlab 0 "no qualification since leaving school" 1 "Degree and postgraduate degrees" 2 "Other
>  qualification"

. label values educ educlab

. 
. ** Employment status
. gen employ=1 if G4==1|G4==2
(815 missing values generated)

. replace employ=2 if G4==3|G4==4|G4==7
(113 real changes made)

. replace employ=3 if G4==5
(617 real changes made)

. replace employ=4 if G4==6
(19 real changes made)

. replace employ=5 if employ==96
(0 real changes made)

. label define employlab 1 "Employed" 2 "Unemployed" 3 "Retired" 4 "Student" 5 "Other"

. label values employ employlab

. 
. ** Income
. gen income=J6

. replace income=. if income==999
(137 real changes made, 137 to missing)

. 
. ** Union membership
. gen union=1 if G6==1
(1,310 missing values generated)

. replace union=0 if G6==2
(1,219 real changes made)

. 
. ** Abstention model
. ** can't control for state because there is insufficient variation on abstention within some states - makes
>  since given v low sample size
. logit abstain c.hostile_rescale2##i.candidate_gender i.female i.educ c.age i.employ c.income c.distance i.i
> ncumbent ///
>         i.union [pw=wt_pooled] if sample_races==1&copartisan==1 

Iteration 0:   log pseudolikelihood = -111.25416  
Iteration 1:   log pseudolikelihood = -105.34277  
Iteration 2:   log pseudolikelihood = -92.264466  
Iteration 3:   log pseudolikelihood = -91.717683  
Iteration 4:   log pseudolikelihood = -91.709768  
Iteration 5:   log pseudolikelihood =  -91.70976  

Logistic regression                             Number of obs     =      1,003
                                                Wald chi2(14)     =      72.85
                                                Prob > chi2       =     0.0000
Log pseudolikelihood =  -91.70976               Pseudo R2         =     0.1757

-----------------------------------------------------------------------------------------------------
                                    |               Robust
                            abstain |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------------+----------------------------------------------------------------
                   hostile_rescale2 |   1.827117   .6250285     2.92   0.003     .6020838    3.052151
                                    |
                   candidate_gender |
                             women  |   .3794149   1.168804     0.32   0.745    -1.911398    2.670228
                                    |
candidate_gender#c.hostile_rescale2 |
                             women  |   -.530588   .8779561    -0.60   0.546     -2.25135    1.190174
                                    |
                             female |
                             women  |   .2760374   .6799294     0.41   0.685      -1.0566    1.608675
                                    |
                               educ |
   Degree and postgraduate degrees  |   -.655576   .8396856    -0.78   0.435     -2.30133    .9901775
               Other qualification  |  -.8618654   .8739378    -0.99   0.324    -2.574752    .8510211
                                    |
                                age |   .0328601   .0330688     0.99   0.320    -.0319536    .0976738
                                    |
                             employ |
                        Unemployed  |   .2520586   1.062818     0.24   0.813    -1.831026    2.335143
                           Retired  |  -1.192979    .986988    -1.21   0.227    -3.127439    .7414823
                           Student  |   2.249722    1.42442     1.58   0.114    -.5420892    5.041533
                                    |
                             income |  -.0141813   .0662782    -0.21   0.831    -.1440842    .1157216
                           distance |   .0158868   .0230981     0.69   0.492    -.0293848    .0611583
                                    |
                          incumbent |
                         incumbent  |  -1.167235   .8412455    -1.39   0.165    -2.816046    .4815755
                            1.union |  -1.365996   .7741343    -1.76   0.078    -2.883271    .1512796
                              _cons |  -6.155115   2.537585    -2.43   0.015    -11.12869    -1.18154
-----------------------------------------------------------------------------------------------------

. outreg using "Output\BJPS resubmission\Table 8 Australia.doc", replace se or ctitles("", "Abstention") star
> levels (10 5 1)

                            -----------------------------------------------------
                                                                     Abstention 
                            -----------------------------------------------------
                             hostile_rescale2                          6.216    
                                                                     (3.885)*** 
                             1.candidate_gender                        1.461    
                                                                      (1.708)   
                             1.candidate_gender#c.hostile_rescale2     0.588    
                                                                      (0.516)   
                             1.female                                  1.318    
                                                                      (0.896)   
                             1bn.educ                                  0.519    
                                                                      (0.436)   
                             2.educ                                    0.422    
                                                                      (0.369)   
                             age                                       1.033    
                                                                      (0.034)   
                             2bn.employ                                1.287    
                                                                      (1.367)   
                             3.employ                                  0.303    
                                                                      (0.299)   
                             4.employ                                  9.485    
                                                                      (13.511)  
                             income                                    0.986    
                                                                      (0.065)   
                             distance                                  1.016    
                                                                      (0.023)   
                             1.incumbent                               0.311    
                                                                      (0.262)   
                             1.union                                   0.255    
                                                                      (0.198)*  
                             N                                         1,003    
                            -----------------------------------------------------
                                       * p<0.1; ** p<0.05; *** p<0.01


. margins candidate_gender, at(hostile_rescale2=(0(1)2))

Predictive margins                              Number of obs     =      1,003
Model VCE    : Robust

Expression   : Pr(abstain), predict()

1._at        : hostile_re~2    =           0

2._at        : hostile_re~2    =           1

3._at        : hostile_re~2    =           2

--------------------------------------------------------------------------------------
                     |            Delta-method
                     |     Margin   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------+----------------------------------------------------------------
_at#candidate_gender |
              1#men  |    .004956   .0036881     1.34   0.179    -.0022725    .0121845
            1#women  |   .0071797   .0064655     1.11   0.267    -.0054924    .0198518
              2#men  |    .028274   .0117829     2.40   0.016       .00518     .051368
            2#women  |   .0246176   .0131545     1.87   0.061    -.0011649       .0504
              3#men  |   .1301981   .0752777     1.73   0.084    -.0173434    .2777397
            3#women  |   .0764265   .0613449     1.25   0.213    -.0438073    .1966603
--------------------------------------------------------------------------------------

. marginsplot, ytitle("Probability of abstaining") xtitle("") xlabel(0 "Not sexist (<1)" 1 "Neutral (1)" 2 "S
> exist (>1)") ///
>         title("Australia") saving("Output\BJPS resubmission\Australia_table8", replace)

  Variables that uniquely identify margins: hostile_rescale2 candidate_gender
(file Output\BJPS resubmission\Australia_table8.gph saved)

. 
. ** Robustness - by party
. logit abstain c.hostile_rescale2##i.candidate_gender i.female i.educ c.age i.employ c.income c.distance i.i
> ncumbent ///
>         i.union [pw=wt_pooled] if sample_races==1&copartisan==1&B1==1|B1==3 

note: 4.employ != 0 predicts failure perfectly
      4.employ dropped and 4 obs not used

Iteration 0:   log pseudolikelihood = -52.888873  
Iteration 1:   log pseudolikelihood = -48.859736  
Iteration 2:   log pseudolikelihood = -47.367838  
Iteration 3:   log pseudolikelihood = -47.337742  
Iteration 4:   log pseudolikelihood = -47.337509  
Iteration 5:   log pseudolikelihood = -47.337509  

Logistic regression                             Number of obs     =        602
                                                Wald chi2(13)     =      48.30
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -47.337509               Pseudo R2         =     0.1050

-----------------------------------------------------------------------------------------------------
                                    |               Robust
                            abstain |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------------+----------------------------------------------------------------
                   hostile_rescale2 |   1.940906   .5734893     3.38   0.001     .8168874    3.064924
                                    |
                   candidate_gender |
                             women  |   1.516882   1.631399     0.93   0.352    -1.680602    4.714366
                                    |
candidate_gender#c.hostile_rescale2 |
                             women  |   -1.17816   .9888058    -1.19   0.233    -3.116184    .7598639
                                    |
                             female |
                             women  |   .0880013   .8928458     0.10   0.921    -1.661944    1.837947
                                    |
                               educ |
   Degree and postgraduate degrees  |  -1.932828   1.357019    -1.42   0.154    -4.592537    .7268803
               Other qualification  |  -1.173203   1.513931    -0.77   0.438    -4.140453    1.794048
                                    |
                                age |  -.0099458    .046344    -0.21   0.830    -.1007784    .0808868
                                    |
                             employ |
                        Unemployed  |  -.2100382    .993289    -0.21   0.833    -2.156849    1.736772
                           Retired  |  -1.767879   1.212992    -1.46   0.145    -4.145299    .6095408
                           Student  |          0  (empty)
                                    |
                             income |   .0422335   .0879529     0.48   0.631     -.130151    .2146181
                           distance |  -.0334959   .0448144    -0.75   0.455    -.1213305    .0543387
                                    |
                          incumbent |
                         incumbent  |  -.7655168   1.132175    -0.68   0.499    -2.984539    1.453505
                            1.union |  -.3480258    1.32975    -0.26   0.794    -2.954287    2.258235
                              _cons |  -4.543144    3.69381    -1.23   0.219    -11.78288    2.696591
-----------------------------------------------------------------------------------------------------

. outreg using "Output\BJPS resubmission\Robustness checks\party turnout Australia.doc", replace se or ctitle
> s("", " c. Liberal-National identifiers") starlevels (10 5 1)

                 ---------------------------------------------------------------------------
                                                           c. Liberal-National identifiers 
                 ---------------------------------------------------------------------------
                  hostile_rescale2                                     6.965               
                                                                     (3.994)***            
                  1.candidate_gender                                   4.558               
                                                                      (7.436)              
                  1.candidate_gender#c.hostile_rescale2                0.308               
                                                                      (0.304)              
                  1.female                                             1.092               
                                                                      (0.975)              
                  1bn.educ                                             0.145               
                                                                      (0.196)              
                  2.educ                                               0.309               
                                                                      (0.468)              
                  age                                                  0.990               
                                                                      (0.046)              
                  2bn.employ                                           0.811               
                                                                      (0.805)              
                  3.employ                                             0.171               
                                                                      (0.207)              
                  income                                               1.043               
                                                                      (0.092)              
                  distance                                             0.967               
                                                                      (0.043)              
                  1.incumbent                                          0.465               
                                                                      (0.527)              
                  1.union                                              0.706               
                                                                      (0.939)              
                  N                                                     602                
                 ---------------------------------------------------------------------------
                                       * p<0.1; ** p<0.05; *** p<0.01


. logit abstain c.hostile_rescale2##i.candidate_gender i.female i.educ c.age i.employ c.income c.distance i.i
> ncumbent ///
>         i.union [pw=wt_pooled] if sample_races==1&copartisan==1&B1==2

note: 0.incumbent != 1 predicts failure perfectly
      0.incumbent dropped and 189 obs not used

note: 0.union != 1 predicts failure perfectly
      0.union dropped and 74 obs not used

note: 1.incumbent omitted because of collinearity
note: 1.union omitted because of collinearity
Iteration 0:   log pseudolikelihood = -45.443975  
Iteration 1:   log pseudolikelihood = -35.982429  
Iteration 2:   log pseudolikelihood = -29.532464  
Iteration 3:   log pseudolikelihood = -27.847867  
Iteration 4:   log pseudolikelihood =  -27.75599  
Iteration 5:   log pseudolikelihood = -27.755289  
Iteration 6:   log pseudolikelihood = -27.755289  

Logistic regression                             Number of obs     =        150
                                                Wald chi2(12)     =      22.07
                                                Prob > chi2       =     0.0367
Log pseudolikelihood = -27.755289               Pseudo R2         =     0.3892

-----------------------------------------------------------------------------------------------------
                                    |               Robust
                            abstain |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------------+----------------------------------------------------------------
                   hostile_rescale2 |   4.343029   2.152737     2.02   0.044     .1237417    8.562317
                                    |
                   candidate_gender |
                             women  |   .6336822   2.964161     0.21   0.831    -5.175966    6.443331
                                    |
candidate_gender#c.hostile_rescale2 |
                             women  |  -1.570474   2.941183    -0.53   0.593    -7.335087    4.194138
                                    |
                             female |
                             women  |   .3822984   .7394709     0.52   0.605    -1.067038    1.831635
                                    |
                               educ |
   Degree and postgraduate degrees  |  -.0550733   1.099914    -0.05   0.960    -2.210865    2.100719
               Other qualification  |  -1.055304   .9413899    -1.12   0.262    -2.900395    .7897858
                                    |
                                age |   .1154863   .0410724     2.81   0.005     .0349859    .1959866
                                    |
                             employ |
                        Unemployed  |   1.868428   1.817259     1.03   0.304    -1.693333     5.43019
                           Retired  |  -2.137522   1.725576    -1.24   0.215    -5.519589    1.244545
                           Student  |   5.358277   1.621222     3.31   0.001      2.18074    8.535814
                                    |
                             income |   .0557654   .1000656     0.56   0.577    -.1403595    .2518904
                           distance |   .0297509   .0396497     0.75   0.453     -.047961    .1074628
                                    |
                          incumbent |
                         incumbent  |          0  (empty)
                            1.union |          0  (empty)
                              _cons |  -13.64678   5.260879    -2.59   0.009    -23.95792   -3.335649
-----------------------------------------------------------------------------------------------------

. outreg using "Output\BJPS resubmission\Robustness checks\party turnout Australia.doc", merge se or ctitles(
> "", " c. Labor identifiers") starlevels (10 5 1)

     --------------------------------------------------------------------------------------------------
                                               c. Liberal-National identifiers   c. Labor identifiers 
     --------------------------------------------------------------------------------------------------
      hostile_rescale2                                     6.965                       76.940         
                                                         (3.994)***                  (165.632)**      
      1.candidate_gender                                   4.558                        1.885         
                                                          (7.436)                      (5.586)        
      1.candidate_gender#c.hostile_rescale2                0.308                        0.208         
                                                          (0.304)                      (0.612)        
      1.female                                             1.092                        1.466         
                                                          (0.975)                      (1.084)        
      1bn.educ                                             0.145                        0.946         
                                                          (0.196)                      (1.041)        
      2.educ                                               0.309                        0.348         
                                                          (0.468)                      (0.328)        
      age                                                  0.990                        1.122         
                                                          (0.046)                    (0.046)***       
      2bn.employ                                           0.811                        6.478         
                                                          (0.805)                     (11.772)        
      3.employ                                             0.171                        0.118         
                                                          (0.207)                      (0.204)        
      income                                               1.043                        1.057         
                                                          (0.092)                      (0.106)        
      distance                                             0.967                        1.030         
                                                          (0.043)                      (0.041)        
      1.incumbent                                          0.465                                      
                                                          (0.527)                                     
      1.union                                              0.706                                      
                                                          (0.939)                                     
      4.employ                                                                         212.359        
                                                                                    (344.281)***      
      N                                                     602                          150          
     --------------------------------------------------------------------------------------------------
                                       * p<0.1; ** p<0.05; *** p<0.01


. 
. ** Robustness - using "women seek to gain power by getting control over men"
. logit abstain c.control##i.candidate_gender i.female i.educ c.age i.employ c.income c.distance i.incumbent 
> ///
>         i.union [pw=wt_pooled] if sample_races==1&copartisan==1 

Iteration 0:   log pseudolikelihood = -111.25119  
Iteration 1:   log pseudolikelihood = -107.97416  
Iteration 2:   log pseudolikelihood = -93.737184  
Iteration 3:   log pseudolikelihood = -92.831334  
Iteration 4:   log pseudolikelihood =  -92.80827  
Iteration 5:   log pseudolikelihood = -92.808185  
Iteration 6:   log pseudolikelihood = -92.808185  

Logistic regression                             Number of obs     =      1,002
                                                Wald chi2(14)     =      72.94
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -92.808185               Pseudo R2         =     0.1658

--------------------------------------------------------------------------------------------------
                                 |               Robust
                         abstain |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
---------------------------------+----------------------------------------------------------------
                         control |   .3423873   .2154924     1.59   0.112      -.07997    .7647446
                                 |
                candidate_gender |
                          women  |  -.1293153   .7655807    -0.17   0.866    -1.629826    1.371195
                                 |
      candidate_gender#c.control |
                          women  |   .5076698   .3261091     1.56   0.120    -.1314924    1.146832
                                 |
                          female |
                          women  |   .2417707   .6724255     0.36   0.719    -1.076159      1.5597
                                 |
                            educ |
Degree and postgraduate degrees  |  -.5989583   .9125274    -0.66   0.512    -2.387479    1.189563
            Other qualification  |  -.6562075   .8804361    -0.75   0.456    -2.381831    1.069416
                                 |
                             age |   .0346443   .0338453     1.02   0.306    -.0316913    .1009799
                                 |
                          employ |
                     Unemployed  |   .2032219   1.110639     0.18   0.855     -1.97359    2.380034
                        Retired  |  -1.255173   1.018787    -1.23   0.218    -3.251958    .7416116
                        Student  |   1.966583   1.480212     1.33   0.184    -.9345798    4.867746
                                 |
                          income |  -.0189966   .0708664    -0.27   0.789    -.1578921    .1198989
                        distance |   .0217568   .0233457     0.93   0.351        -.024    .0675135
                                 |
                       incumbent |
                      incumbent  |  -1.035669   .9324602    -1.11   0.267    -2.863257    .7919196
                         1.union |  -1.506541   .8786748    -1.71   0.086    -3.228712    .2156298
                           _cons |  -4.359768   2.882792    -1.51   0.130    -10.00994      1.2904
--------------------------------------------------------------------------------------------------

. outreg using "Output\BJPS resubmission\Robustness checks\control turnout Australia.doc", replace se or ctit
> les("", "Abstention") starlevels (10 5 1)

                                --------------------------------------------
                                                                Abstention 
                                --------------------------------------------
                                 control                          1.408    
                                                                 (0.303)   
                                 1.candidate_gender               0.879    
                                                                 (0.673)   
                                 1.candidate_gender#c.control     1.661    
                                                                 (0.542)   
                                 1.female                         1.274    
                                                                 (0.856)   
                                 1bn.educ                         0.549    
                                                                 (0.501)   
                                 2.educ                           0.519    
                                                                 (0.457)   
                                 age                              1.035    
                                                                 (0.035)   
                                 2bn.employ                       1.225    
                                                                 (1.361)   
                                 3.employ                         0.285    
                                                                 (0.290)   
                                 4.employ                         7.146    
                                                                 (10.578)  
                                 income                           0.981    
                                                                 (0.070)   
                                 distance                         1.022    
                                                                 (0.024)   
                                 1.incumbent                      0.355    
                                                                 (0.331)   
                                 1.union                          0.222    
                                                                 (0.195)*  
                                 N                                1,002    
                                --------------------------------------------
                                       * p<0.1; ** p<0.05; *** p<0.01


. 
. ** Controlling for previous vote share
. logit abstain c.hostile_rescale2##i.candidate_gender i.female i.educ c.age i.employ c.income c.voteshare201
> 6 i.incumbent ///
>         i.union [pw=wt_pooled] if sample_races==1&copartisan==1 

Iteration 0:   log pseudolikelihood = -111.25416  
Iteration 1:   log pseudolikelihood = -105.20627  
Iteration 2:   log pseudolikelihood = -92.003265  
Iteration 3:   log pseudolikelihood = -91.438685  
Iteration 4:   log pseudolikelihood = -91.430424  
Iteration 5:   log pseudolikelihood = -91.430414  
Iteration 6:   log pseudolikelihood = -91.430414  

Logistic regression                             Number of obs     =      1,003
                                                Wald chi2(14)     =      73.46
                                                Prob > chi2       =     0.0000
Log pseudolikelihood = -91.430414               Pseudo R2         =     0.1782

-----------------------------------------------------------------------------------------------------
                                    |               Robust
                            abstain |      Coef.   Std. Err.      z    P>|z|     [95% Conf. Interval]
------------------------------------+----------------------------------------------------------------
                   hostile_rescale2 |   1.846316   .6121615     3.02   0.003      .646501     3.04613
                                    |
                   candidate_gender |
                             women  |   .5619565    1.08042     0.52   0.603    -1.555627     2.67954
                                    |
candidate_gender#c.hostile_rescale2 |
                             women  |  -.7299773   .7984098    -0.91   0.361    -2.294832    .8348771
                                    |
                             female |
                             women  |   .2628416   .6606676     0.40   0.691    -1.032043    1.557726
                                    |
                               educ |
   Degree and postgraduate degrees  |  -.6456759   .8478311    -0.76   0.446    -2.307394    1.016042
               Other qualification  |   -.886589   .8854258    -1.00   0.317    -2.621992    .8488136
                                    |
                                age |   .0321293   .0335373     0.96   0.338    -.0336027    .0978612
                                    |
                             employ |
                        Unemployed  |   .2768904   1.054189     0.26   0.793    -1.789282    2.343063
                           Retired  |  -1.164986   .9564141    -1.22   0.223    -3.039523    .7095517
                           Student  |   2.333233   1.485882     1.57   0.116    -.5790423    5.245508
                                    |
                             income |   -.015505   .0675897    -0.23   0.819    -.1479784    .1169685
                      voteshare2016 |  -.0166199   .0210446    -0.79   0.430    -.0578666    .0246269
                                    |
                          incumbent |
                         incumbent  |  -1.100617   .7906114    -1.39   0.164    -2.650187    .4489524
                            1.union |  -1.371717   .7703404    -1.78   0.075    -2.881556    .1381228
                              _cons |  -5.490414   2.567756    -2.14   0.032    -10.52312   -.4577041
-----------------------------------------------------------------------------------------------------

. outreg using "Output\BJPS resubmission\Robustness checks\vote share control turnout Australia.doc", replace
>  se or ctitles("", "Abstention") starlevels (10 5 1)

                            -----------------------------------------------------
                                                                     Abstention 
                            -----------------------------------------------------
                             hostile_rescale2                          6.336    
                                                                     (3.879)*** 
                             1.candidate_gender                        1.754    
                                                                      (1.895)   
                             1.candidate_gender#c.hostile_rescale2     0.482    
                                                                      (0.385)   
                             1.female                                  1.301    
                                                                      (0.859)   
                             1bn.educ                                  0.524    
                                                                      (0.445)   
                             2.educ                                    0.412    
                                                                      (0.365)   
                             age                                       1.033    
                                                                      (0.035)   
                             2bn.employ                                1.319    
                                                                      (1.390)   
                             3.employ                                  0.312    
                                                                      (0.298)   
                             4.employ                                  10.311   
                                                                      (15.321)  
                             income                                    0.985    
                                                                      (0.067)   
                             voteshare2016                             0.984    
                                                                      (0.021)   
                             1.incumbent                               0.333    
                                                                      (0.263)   
                             1.union                                   0.254    
                                                                      (0.195)*  
                             N                                         1,003    
                            -----------------------------------------------------
                                       * p<0.1; ** p<0.05; *** p<0.01


. 
. ** Abstention models
. use "Data\Australia_data_recoded.dta", clear 

. 
. ** 80% turnout in the weighted sample
. ** Abstainers
. gen abstain=1 if B9_1==6
(6,494 missing values generated)

. replace abstain=0 if B9_1!=6&B9_1!=999
(6,328 real changes made)

. 
. 
. ** Abstainers versus voters on sexism
. collapse abstain hostile_rescale [iw=wt_pooled], by(ID)

. drop if hostile_rescale==.
(30 observations deleted)

. 
. gen nothostile=1 if hostile_rescale<0
(976 missing values generated)

. replace nothostile=0 if hostile_rescale>=0
(976 real changes made)

. gen hostile2=1 if hostile_rescale>0
(1,511 missing values generated)

. replace hostile2=0 if hostile_rescale<=0
(1,511 real changes made)

. 
. tab abstain hostile2, row

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

    (mean) |       hostile2
   abstain |         0          1 |     Total
-----------+----------------------+----------
         0 |     1,454        589 |     2,043 
           |     71.17      28.83 |    100.00 
-----------+----------------------+----------
         1 |        29         19 |        48 
           |     60.42      39.58 |    100.00 
-----------+----------------------+----------
     Total |     1,483        608 |     2,091 
           |     70.92      29.08 |    100.00 


. tab abstain nothostile, row

+----------------+
| Key            |
|----------------|
|   frequency    |
| row percentage |
+----------------+

    (mean) |      nothostile
   abstain |         0          1 |     Total
-----------+----------------------+----------
         0 |       917      1,126 |     2,043 
           |     44.88      55.12 |    100.00 
-----------+----------------------+----------
         1 |        29         19 |        48 
           |     60.42      39.58 |    100.00 
-----------+----------------------+----------
     Total |       946      1,145 |     2,091 
           |     45.24      54.76 |    100.00 


. 
. log close
      name:  <unnamed>
       log:  C:\Users\a15014rs\Dropbox\Sexism and Candidates\Analysis\BJPS replication files\Australia_log_fi
> le.log
  log type:  text
 closed on:  20 Mar 2025, 11:25:11
-------------------------------------------------------------------------------------------------------------
